Using multi-state markov models to identify credit card risk

AUTOR(ES)

Régis, Daniel Evangelista, Artes, Rinaldo

FONTE

Prod.

DATA DE PUBLICAÇÃO

24/11/2015

RESUMO

Abstract The main interest of this work is to analyze the application of multi-state Markov models to evaluate credit card risk by investigating the characteristics of different state transitions in client-institution relationships over time, thereby generating score models for various purposes. We also used logistic regression models to compare the results with those obtained using multi-state Markov models. The models were applied to an actual database of a Brazilian financial institution. In this application, multi-state Markov models performed better than logistic regression models in predicting default risk, and logistic regression models performed better in predicting cancellation risk.

Documentos Relacionados

• Aplicação do Modelo Multi-estado de Markov em Cartões de Crédito
• Analytical description of the activation of multi-state receptors by continuous neurotransmitter signals at brain synapses.
• Using multi-data hidden Markov models trained on local neighborhoods of protein structure to predict residue–residue contacts
• A systematic approach to construct credit risk forecast models
• Credit card risk behavior on college campuses: evidence from Brazil